Artimagen 0.91

Library for Artificial Charged-Particle-Microscopy Image Generation for C++
artimagen (Artificial Scanning Electron Microscope Image Generator) is a library that can generate artificial SEM (and other charged-particle microscopy) images of various samples, including gold-on-carbon resolution sample, or some semiconductor structures. Numerous effects that appear in real SEMs are simulated (noise, drift-distortion, edge-effect, etc.), which enables assessment of imaging, metrology or other techniques that work with SEM micrographs. Unlike the real SEM images, the artificial images exhibit defined types and amounts of these effects, which is their key advantage.

The Artificial SEM Image Generator has been developed by Petr Cizmar at the U.S. National Institute of Standards and Technology. The first version of the generator was written in C to evaluate the ISO-candidate image-sharpness-calculation techniques. Later, the Artificial SEM Image Generator became needed for other applications as well and support for new kinds of samples was added. In order to make the generator more flexible, it was rewritten to C++. The users now may take advantage of the modular object structure of the program.

At this time, also programs written in C or other languages may be linlked with the library, which is enabled by the C wrapper. The basic usage of the library is quite easy (see the Howto).

As an example of the artimagen library usage, the gAIG program was written, which is a GUI front end to the library (demonstrating just the basic usage). It can be obtained from the download section. The program is multi-platform, it can be compiled for Windows32, Linux (32-bit and 64-bit versions), MacOSX, and others (untested).

last updated on:
June 24th, 2010, 9:27 GMT
price:
FREE!
homepage:
artimagen.sourceforge.net
license type:
Public Domain 
developed by:
Petr Cizmar
category:
ROOT \ Science and Engineering \ Mathematics
Artimagen
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What's New in This Release:
  • Lua scripting support
  • Fast and rigorous algorithms for Gaussian and Poisson noise
  • Faster feature generation using Simulation-of-Simplicity-based algorithms
  • New version (0.2) of gAIG released
read full changelog

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